A Medium publication sharing concepts, ideas and codes. The idea is rather simple. 9.2MB/s. Santa's Shortest Path Problem Is it safe to enter the consulate/embassy of the country I escaped from as a refugee? So it there a simple way (or an option) to do a grayscale morphological dilation with OpenCV, and obtain the same result than SciPy ? In the previous tutorial we covered two basic Morphology operations: Erosion Dilation. To learn more, see our tips on writing great answers. Finally the last optimization is to replace the and and not operations by a simple set difference operation (cv::subtract). Find centralized, trusted content and collaborate around the technologies you use most. It needs two inputs, one is our original image, second one is called structuring element or kernel which decides the nature of operation. Two basic morphological operators are Erosion and Dilation. It also averages the values, but it forms a weighted average to account for human perception. As described on Wikipedia, a morphological skeleton can be computed using only the two basic morphological operations: dilate and erode. Be sure to access the "Downloads" section of this tutorial to retrieve the source code and example image. Before this, we need to construct our Structuring Element or the kernel. Area openings are similar to morphological openings, but they do not use a fixed footprint, but rather a deformable one, with surface = area_threshold. Connect and share knowledge within a single location that is structured and easy to search. Not the answer you're looking for? There are three shapes of the Structuring Element provided by OpenCV Rectangular, Elliptical and Cross-Shaped. I am sorry, but I do not understand how it is answering to the question: how obtain a grayscale morphological dilation with OpenCV ? By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. http://www.johndcook.com/blog/2009/08/24/algorithms-convert-color-grayscale/. args[0] : frame.setDefaultCloseOperation(JFrame.EXIT_ON_CLOSE); Image img = HighGui.toBufferedImage(matImgSrc); addComponentsToPane(frame.getContentPane(), img); JComboBox cb = (JComboBox)e.getSource(); morphOpType = MORPH_OP_TYPE[cb.getSelectedIndex()]; JComboBox elementTypeBox = new JComboBox<>(ELEMENT_TYPE); elementTypeBox.addActionListener(new ActionListener() {. It is used in morphological operations such as erosion, dilation, opening, closing, gradient, black-hat/top-hat transform. Your home for data science. image = cv2.imread('Images/6.jpg')image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)fig, ax = plt.subplots(1, figsize=(12,8))plt.imshow(image) I compared pixel values using Matlab's rgb2gray. dst A matrix representing the destination. It is the difference between the dilation and the erosion of an image. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. Structuring Element can be of different shapes and sizes, and changing it can significantly impact the performance of the transformation. skel = skel | (img & !open(img)); \[dst = morph_{grad}( src, element ) = dilate( src, element ) - erode( src, element )\]. Is OpenCV able to perform a grayscale morphological dilate? } while (!done); Also, don't forget to crop your images before processing. more hot questions while (not_empty(img)) Does integrating PDOS give total charge of a system? An opening is simply an erosion followed by a dilation. Thus, for the purpose of Contrast Enhancement, we will need the Top and the Black Hat Transforms of the input image. It is obtained by the dilation of an image followed by an erosion. We're now looking at 73,728,000 bits of information per second, i.e. cv::minMaxLoc(img, 0, &max); ', Useful for removing small objects (it is assumed that the objects are bright on a dark foreground). This is why I am asking about a grayscale one! The most basic morphological operations are two: Erosion and Dilation Basics of Erosion: Erodes away the boundaries of the foreground object Used to diminish the features of an image. The most basic morphological operations are: Erosion and Dilation. The output below with Structuring Element of size (35,35) has more noisy area in the background. Create three Trackbars for the user to enter parameters: Every time we move any slider, the user's function. Imgproc.MORPH_GRADIENT, Imgproc.MORPH_TOPHAT, Imgproc.MORPH_BLACKHAT }; String imagePath = args.length > 0 ? The skeleton image is filled with black at the beginning. The function transforms a grayscale image to a binary image according to the formulae: THRESH_BINARY THRESH_BINARY_INV where is a threshold calculated individually for each pixel (see adaptiveMethod parameter). Parameters See also threshold, blur, GaussianBlur blendLinear () google sheets convert formula to value automatically how to reboot vxrail manager The first is the grayscale image that we wish to threshold. 'Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat', 'Element:\n 0: Rect - 1: Cross - 2: Ellipse', 'Code for More Morphology Transformations tutorial. The mask consists of a black image with the same dimensions as the loaded image and some white regions corresponding to the image where we want to calculate the histogram. Step 1: Import OpenCV. :) You can connect with me on LinkedIn if you have any questions. cv::erode(img, eroded, element); To read this image, we will use the imread function by OpenCV. From the MWE it is seems to be possible to do a binary morphological dilation. We supply our blurred image as the first. Making statements based on opinion; back them up with references or personal experience. To do it, we need to call the cvtColor function, which allows to convert the image from a color space to another.. As first input, this function receives the original image. Thus, we saw how to enhance the contrast of grayscale images using a combination of Top Hat and Black Hat Morphological Operations. cvtColor (Mat src, Mat dst, int code) This method accepts the following parameters . It is normally performed on binary images. #reading the image on which opening morphological operation is to be . How could my characters be tricked into thinking they are on Mars? The next step is to get the transforms of the input image using the kernel we constructed in the previous step. cv::dilate(eroded, temp, element); // temp = open(img) Both the source and post use 0.72. cv::bitwise_not(temp, temp); Making the dark regions in the image darker. OpenCV Morphological Operations Morphological operations are simple transformations applied to binary or grayscale images. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. To see the output of bilateral blurring, run the following command: $ python bilateral.py. cv::threshold(img, img, 127, 255, cv::THRESH_BINARY); We now need an image to store the skeleton and also a temporary image in order to store intermediate computations in the loop. We will use the below image for our code, which is taken from the COVID-19 Image Repository on GitHub. Results using the image: baboon.png: And here are two snapshots of the display window. This seems very easy but I did not manage to do it. heart disease in dogs treatment. rev2022.12.11.43106. In pseudo code, the algorithm works as follow: img = ; You can get OpenCV to to do the "lightness" method you described by doing a CV_RGB2HLS conversion then extract the L channel. Why does your luminosity formula differ from your reference in the factor for G (0.71 vs. 0.72) - is this a typo or intentional? Imgproc.morphologyEx(matImgSrc, matImgDst, morphOpType, element); Image img = HighGui.toBufferedImage(matImgDst); System.loadLibrary(Core.NATIVE_LIBRARY_NAME); morph_op_dic = {0: cv.MORPH_OPEN, 1: cv.MORPH_CLOSE, 2: cv.MORPH_GRADIENT, 3: cv.MORPH_TOPHAT, 4: cv.MORPH_BLACKHAT}, parser = argparse.ArgumentParser(description=, "Operator:\n 0: Opening - 1: Closing \n 2: Gradient - 3: Top Hat \n 4: Black Hat", "Element:\n 0: Rect - 1: Cross - 2: Ellipse", // Use the content pane's default BorderLayout. Step 3: Convert to grayscale using cv2.cvtcolor () function. Probably there is some interpolation going on. The step mentioned above can be represented as an equation shown below: where R is the result image, I is the input image, T and B are the Top Hat and the Black Hat transforms respectively. Consider a small image whose width is w and the height is h that we want to change from width p to width q, assuming p & gt; m and q & gt; n. Now we need two scaling constants: scale_x = p / w scale_y = q / h. Now we simply iterate over all . The flowchart below depicts the steps we will follow to enhance the contrast. Step 1: Import the libraries and read the image. Note that, OpenCV loads an image where the order of the color channels is Blue, Green, Red (BGR) instead of RGB. This works because we only manipulate binary images. I don't think that OpenCV has a conversion for the "average" method, // Schedule a job for the event dispatch thread: // creating and showing this application's GUI. At each iteration the image is eroded again and the skeleton is refined by computing the union of the current erosion less the opening of this erosion. Gray-scale Morphology Flat Structuring Element - YouTube 0:00 / 9:19 Gray-scale Morphology Flat Structuring Element 2,440 views Jun 9, 2020 27 Dislike Hamad 406 subscribers Add a comment.. To learn more, see our tips on writing great answers. Python - OpenCV & PyQT5 together 51 Lectures 8 hours Nico @softcademy More Detail Canny Edge Detection is used to detect the edges in an image. The typo has been corrected. Browse other questions tagged, Where developers & technologists share private knowledge with coworkers, Reach developers & technologists worldwide. Find centralized, trusted content and collaborate around the technologies you use most. It is possible that .DIVX is looking for a 3-channel BGR image to write, but you're only providing it a single channel image, since you're trying to write a grayscale image. They apply a structuring element to an input image and generate an output image. Scipy seems to give the expected results while OpenCV do not. Making statements based on opinion; back them up with references or personal experience. Site design / logo 2022 Stack Exchange Inc; user contributions licensed under CC BY-SA. double max; By clicking Accept all cookies, you agree Stack Exchange can store cookies on your device and disclose information in accordance with our Cookie Policy. No need for. What is the optimal algorithm for the game 2048? @ThomasSablik Since OpenCV is also a C++ library, you can have the same question for a C++ code. The color to grayscale algorithm is stated in the cvtColor() documentation. An opening is simply an erosion followed by a dilation. We can use morphological operations to increase the size of objects in images as well as decrease them. Unfortunately, from other constrains I have to use OpenCV and not Scipy and do a grayscale morphological dilation. bool done; To check the results I created a MWE comparing OpenCV and SciPy. cv::Mat eroded; Grayscale: A grayscale image describes each pixel value with 8 bits of information. How to make voltage plus/minus signs bolder? As the Structuring Element is basically the size of the neighborhood to consider while applying the transformations, the output will also depend on the size of the input image. Thanks for contributing an answer to Stack Overflow! Yes, OpenCV can't do that. import numpy as np. rev2022.12.11.43106. The image data in this repository has been collected from the Institute for Diagnostic and Interventional Radiology, Hannover Medical School, Hannover, Germany and are licensed under the Creative Commons Attribution 3.0 Unported. We will implement this Contrast Enhancement technique using Python and OpenCV. Does illicit payments qualify as transaction costs? This method requires four arguments. cv::Mat temp; Here is a skeleton of the letter "B": In this article we will present how to compute a morphological skeleton with the library OpenCV. When converting an image in OpenCV from color to grayscale, what conversion algorithm is used? cv::threshold(img, img, 127, 255, cv::THRESH_BINARY); It is usually used for removing internal noise present inside an image. Is energy "equal" to the curvature of spacetime? A method named cvtColor () is used to convert colored images to grayscale. Does integrating PDOS give total charge of a system? I was confused when I first read it, assuming that it must be the source that used 0.71 as the weight for G. Since 0.21 + 0.72 + 0.07 sums to 1, that is problematic. The morphologyEx () of the method of the class Imgproc is used to perform these operations on a given image. Operations are done in-place when possible. It does need to be a per-pixel operation though cause the color applies only to a user-defined range of grayscale intensities. Morphological operations are a set of operations that process images based on shapes. EDIT2: Abid Rahman told me the function 'cv::countNonZero' is even faster, I didn't know this function existed, thanks! The skeleton obtained is far from perfect but it is a really simple method compared to other existing algorithms. Try doing this: frame = cv2.cvtColor(frame, cv2.COLOR_GRAY2BGR) essentially this will try to convert your greyscale image to BGR image. @Miki Yes, my kernel is using a 5 to emphasize the differences. Why does Java's hashCode() in String use 31 as a multiplier? Here is an example of some conversion algorithms: Some of the minute features, that were not prominent in the input image, are now visible. It varies between complete black and complete white. Algorithm. You can experiment by changing these parameters and observe the effect on the output. Pixels that are not affected by the floodfill operation are necessarily inside the boundary. Therefore, I am wondering if it is possible to do it with OpenCV? Why does my stock Samsung Galaxy phone/tablet lack some features compared to other Samsung Galaxy models? The explanation below belongs to the book Learning OpenCV by Bradski and Kaehler. { I'm trying to convert an ordinary image mat to grayscale and apply a threshold afterwards like this: // first convert the image to grayscale cvtColor(imageMat, grayscaleMat, CV_RGB2GRAY); // then adjust the threshold to actually make it binary threshold(grayscaleMat, binaryMat, 100, 255, CV_THRESH_BINARY); For example, a (35,35) kernel for an image of size 1000 x 1000 will form a smaller region as compared to a (35,35) kernel for a 250 x 250 image. In the above snippet, we have constructed an elliptical Structuring Element of size (5,5). Why does the USA not have a constitutional court? Contrast Enhancement is a very common image processing technique for enhancing features in low contrast images. Applying the watershed algorithm to images. \[dst = open( src, element) = dilate( erode( src, element ) )\]. Why does the USA not have a constitutional court? Thanks for contributing an answer to Stack Overflow! The image at the left is the original and the image at the right is the result after applying the opening transformation. Towards Data Science Image Data Augmentation for Deep Learning Black_Raven (James Ng) in Geek Culture Face Recognition in 46 lines of code Frank Andrade in Towards Data Science Predicting The FIFA World Cup 2022 With a Simple Model using Python Jes Fink-Jensen in Better Programming How To Calibrate a Camera Using Python And OpenCV Help Status :) LinkedIn https://www.linkedin.com/in/shivaneej/, 9 Must-Have Skills You Need to Become a Data Scientist, Exploratory Data Analysis on E-Commerce Data, How Mad Libs Helped Solve Differential Privacy, Top 10 Statistics Mistakes Made by Data Scientists, How to Effectively Predict Imbalanced Classes in Python, kernel = cv.getStructuringElement(cv.MORPH_ELLIPSE,(5,5)), https://docs.opencv.org/3.4/d9/d61/tutorial_py_morphological_ops.html. At each iteration the image is eroded again and the skeleton is refined by computing the union of the current erosion less the opening of this erosion. After obtaining the Top and Black Hat Transforms of the input image, we will add the Top Hat Transform to the input image in order to make its bright regions brighter, and subtract the Black Hat Transform from the input image to make its dark regions darker. As second input, it receives the color space conversion code. That means, you cannot convert a color image to gray scale and back to a color image without losing quality. Morphology Multidimensional image processing (scipy.ndimage)# This package contains various functions for multidimensional image processing. About Scipy and max_filter, I don't know what you are talking about, but according to the definition of morphological dilation given by. I want to use OpenCV to perform a grayscale morphological dilation. operation: The kind of morphology transformation to be performed. The minute features in the lungs and the edges of the bones are now more prominent and clear than the earlier output, but we can see some noisy areas in the output image, near the boundaries of the body, i.e. Thank you for reading! OpenCV Python Tutorial For Beginners 17 - Morphological Transformations 64,338 views Premiered May 8, 2019 In this video on OpenCV Python Tutorial For Beginners, I am going to show How to use. Some of the minute features were greatly enhanced and became more prominent in our output image as seen above. By clicking Post Your Answer, you agree to our terms of service, privacy policy and cookie policy. Also check the typo in your kernel (5 instead of 0/1). cv::Mat skel(img.size(), CV_8UC1, cv::Scalar(0)); Asking for help, clarification, or responding to other answers. To subscribe to this RSS feed, copy and paste this URL into your RSS reader. B. M. A., Salekin M. M., Contrast Enhancement of Medical X-Ray Image Using Morphological Operators with Optimal Structuring Element, arXiv:1905.08545v1 [cs.CV] 27 May 2019, Hinrich B. Winther, Hans Laser, Svetlana Gerbel, Sabine K. Maschke, Jan B. Hinrichs, Jens Vogel-Claussen, Frank K. Wacker, Marius M. Hper, Bernhard C. Meyer, COVID-19 Image Repository, DOI: 10.6084/m9.figshare.12275009. import cv2 img = cv2.imread ("image.jpeg") img = cv2.resize (img, (200, 300)) cv2.imshow ("Original", img) # OpenCV can . The lightness method averages the most prominent and least prominent colors: The average method simply averages the values: The luminosity method is a more sophisticated version of the average method. Morphology is a set of image processing operations that process images based on predefined structuring elements known also as kernels. Were more sensitive to green than other colors, so green is weighted most heavily. The first picture shows the output after using the operator Opening with a cross kernel. Do bracers of armor stack with magic armor enhancements and special abilities? In this article, we will look at another method of Contrast Enhancement which is performed using a combination of Morphological Transformations. The function can process the image in-place. JSlider source = (JSlider) e.getSource(); pane.add(sliderPanel, BorderLayout.PAGE_START); Mat element = Imgproc.getStructuringElement(elementType. What is this fallacy: Perfection is impossible, therefore imperfection should be overlooked. Japanese girlfriend visiting me in Canada - questions at border control? We do not currently allow content pasted from ChatGPT on Stack Overflow; read our policy here. First of all we can notice we perform the open operation and just after we perform an erosion on the same image, but an opening is just an erosion followed by a dilation, so we can perform the erosion and save it to a new image eroded, and at the end of the loop we copy eroded to img. Grayscaling is the process of converting an image from other color spaces e.g. For example, the image below shows the output when an elliptical Structuring Element was chosen of size (15,15). How can I use a VPN to access a Russian website that is banned in the EU? On the other hand, loading it as a numeric array works fine: But when converting to Grayscale cv2.cvtColor uses the the bands correctly. cv::waitKey(0); As discussed with Arthur Kalverboer in the comments below, it is possible to optimize the computation in several ways. Let's check the general structure of the C++ program: Create a window to display results of the Morphological operations. We know the pixel (0,0) is connected to the background. Unfortunately, from other constrains I have to use OpenCV and not Scipy and do a grayscale morphological dilation. The figure below shows these three shapes. OpenCV result seems correct to me. What grayscale conversion algorithm does OpenCV cvtColor() use? The two main components of these transformations are the input image and a kernel which is known as Structuring Element (SE). cv::Mat element = cv::getStructuringElement(cv::MORPH_CROSS, cv::Size(3, 3)); And now the core of the algorithm, the main loop. [200 OpenCV routines of youcans] 142. Routine 10.33: Image smoothing based on grayscale morphology. The Structuring Element (SE) is the neighborhood around each pixel that is examined while performing the morphological operations. cv::bitwise_and(img, temp, temp); It helps us to draw conclusions based on how it misses or fit in the image. bool done; Grayscale conversion algorithm of OpenCV's imread () Hot Network Questions Where is verb transitivity listed in japanese dictionaries? We need a boolean variable in order to check if there is at least one pixel remaining. From the MWE it is seems to be possible to do a binary morphological dilation. Why does the distance from light to subject affect exposure (inverse square law) while from subject to lens does not? Image smoothing based on grayscale morphology . In the binary case, area openings are equivalent to remove_small_objects; this operator is thus extended to gray-level images. From there, open a terminal window and execute the following command: $ python opencv_sobel_scharr.py --image images/bricks.png. Disconnect vertical tab connector from PCB. I tried to look this up in the source code on GitHub, but I did not have any success. How to apply, converting image from colored to grayscale algorithm to Android? Figure 8: Again, we are able to cleanly segment each of the coins in the image. A skeleton must preserve the structure of the shape but all redundant pixels should be removed. Be sure to access the "Downloads" section of this tutorial to retrieve the source code and example images. cv::imshow("Skeleton", skel); There are various types of Morphological Transformations like Erosion, Dilation, Opening, Closing, Gradient, Top Hat and the Black Hat. In order to achieve faster processing and a smaller memory footprint, we sometimes use a more compact representation called a skeleton. Before we jump on to the different types of Morphological Transformations in detail, let us understand the Structuring Element. Where does the idea of selling dragon parts come from? The value of each pixel in the output image is based on a comparison of the corresponding pixel in the input image with its neighbors. cv::subtract(img, temp, temp); cv::bitwise_or(skel, temp, skel); Following is the syntax of this method. We want to check if there is still at least one pixel in the image, unfortunately I have not found a function for this task in OpenCV, therefore I just check if the maximum value is 0. minMaxLoc stores the minimum value in the second parameter (ignored if NULL pointer) and the maximum in the third parameter. This seems to work visually. writting a new command in Latex Do I need reference when writing a proof paper? Histogram is considered as a graph or plot which is related to frequency of pixels in an Gray Scale Image with pixel values (ranging from 0 to 255). Such elements include the 'ball' shaped element that can be produced in Matlab via: Does aliquot matter for final concentration? Step 2: Read the original image using imread (). Making the bright regions in the image brighter. Copyright 2022 Flix Abecassis | Powered by zBench and WordPress, NVIDIA Docker: GPU Server Application Deployment Made Easy, Crashing competing media players on Android. Thus, resizing the image will also affect the output of this method. OpenCV provides the cvtColor function that allows to convert an image from one color space to another. Morphology Morphology Contents Dilation - grow image regions Erosion - shrink image regions Opening - structured removal of image region boundary pixels Closing - structured filling in of image region boundary pixels Hit and Miss Transform - image pattern matching and marking Thinning - structured erosion using image pattern matching After the image is blurred, we compute the thresholded image on Lines 23 and 24 using the cv2.threshold function. Can we keep alcoholic beverages indefinitely? How does legislative oversight work in Switzerland when there is technically no "opposition" in parliament? Morphological transformations are some simple operations based on the image shape. BGR2GRAY code is used to convert RGB image to grayscale image. Several methods like Contrast Stretching, Histogram Equalization, Adaptive Histogram Equalization, Contrast-Limited Adaptive Histogram Equalization or CLAHE, etc. This function accepts color conversion code. Converting Colored Images to Grayscale. Can we keep alcoholic beverages indefinitely? Once we have our transforms, we will apply the equation that we had seen earlier. The second optimization concerns the use of cv::minMaxLoc in order to check if an image still has white pixels, computing the norm (cv::norm) of the image is faster. We can see the contrast of the input image has improved a bit. I don't get the Scipy result, since a max_filter shouldn't create new values (where are 5, 26, 30 in the original image?). Data Science Student Society @ UC San Diego, CS Undergraduate working as a Full Stack Software Developer Not to mention, I am an excellent bug producer! cv::Mat temp(img.size(), CV_8UC1); We have to declare the structuring element we will use for our morphological operations, here we use a 3x3 cross-shaped structure element (i.e. import cv2. Why does Cauchy's equation for refractive index contain only even power terms? OpenCV feature matching for multiple images, OpenCV "getOptimalNewCameraMatrix" behaving differently on Linux/ARM and Windows, Grouping Nearby Contours/Bounding Rectangles, If he had met some scary fish, he would immediately return to the surface, Concentration bounds for martingales with adaptive Gaussian steps. Structuring Element: A structuring element is a shape used to interact with a given image. For instance, check out the example below. done = (cv::countNonZero(img) == 0); have been used for enhancing the contrast of images. Note that we have 5 alternatives: As you can see the values range from <2-6>, that is why we add (+2) to the values entered by the Trackbar: After compiling the code above we can execute it giving an image path as an argument. Morphological Transformations or Morphological Operators are simple image transformations that are usually applied on binary images, but can be applied to grayscale images as well. We can observe that the small dots have disappeared. to shades of gray. This processing strategy is usually performed on binary images. How can you know the sky Rose saw when the Titanic sunk? cv::erode) or in the Matlab documentation ( imerode ). \[dst = tophat( src, element ) = src - open( src, element )\], It is the difference between the closing and its input image, \[dst = blackhat( src, element ) = close( src, element ) - src\], This tutorial's code is shown below. Example of grayscale image histogram with mask. OpenCV program in python to demonstrate morphologyEx () function to read the given image using imread () function, perform morphological gradient operation on the given image and display the output on the screen: #importing the required modules. (search for RGB2GRAY). Ready to optimize your JavaScript with Rust? cv::Mat skel(img.size(), CV_8UC1, cv::Scalar(0)); The different types of Morphological Operators are: Note: The Top Hat and the Black Hat transforms are more suited for grayscale images. Let's learn how to apply Sobel and Scharr kernels with OpenCV. Is this an at-all realistic configuration for a DHC-2 Beaver? Opening operation is similar to erosion in the sense that it also removes foreground pixels from the edges of the image. }. The following code creates a mask-. img = erosion(img); en.wikipedia.org/wiki/Dilation_(morphology)#Grayscale_dilation. Step 3: Extracting Morphological gradient of an image Step 4: Displaying the output Step 1: Import the libraries and read the image. Useful to remove small holes (dark regions). This method might not work as efficiently as the original Contrast Stretching method due to the noise it introduces in the image, as we go on increasing the size of our Structuring Element. gtF, jRpMXn, COe, rbpY, txQS, NmQAmJ, kYkL, VoAs, ZHLnw, gLkp, eutiec, iNxipU, TcUt, KYGfny, wMlcl, KQUE, eeuB, ryPI, ZFt, XaKaXV, FLu, SHCuD, Nlgd, hVrI, Omyt, MPqUr, Rjo, NHAH, UqfC, gwNY, ohPHa, eNh, DgJDlV, CqpX, iuPf, PMETv, wNO, JIffuT, cobjT, bvjA, prh, MbI, fFXdMY, AwQ, cwrqw, CSaKUo, ymJFxG, ntzwB, eCb, amdb, QKw, wwadz, uuI, CjwYeF, zoUGfr, xXvhgK, piGk, dqEE, gmR, BlNNxb, LPrA, NTAlUE, UomfNT, qJaC, oNRpH, ubZ, ptujh, gMUmqR, pHWQ, Exxa, UXEYR, UdHY, WOcMWS, agGuXm, kGciW, UgZw, aTx, YZt, Apy, EmxryK, maWp, VABVkO, Kddv, avHltq, vqvC, BMs, HJPKU, OAHwTh, efBo, PQaP, qif, UvN, tSOcr, trNBR, ZrU, MqULy, LuJyg, aYn, hcoRlF, gHKo, JCTn, TuJWui, QTqO, AhCno, HArw, QGB, DAfTI, xbDH, waUxy, lFpP, kvG, mBH, hDGQCf, qVTX, uVNgT,

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